Interpreting images of line drawings: a tutorial presented at the 16th International conference on pattern recognition (ICPR), Quebec city, Canada, 11th August 2002
Bibliographic record
Abstract
While development continues, multimedia tools for planning and recording the results of work on complex engineering products and projects are now widely available. These tools can significantly improve communication within project teams but suffer from an input bottleneck: most of the necessary 3D and other product/design information is readily available, but is typically in the form of paper documents, particularly drawings. Manual input of drawings into CAD, GIS and other systems is a possibility, albeit a slow and expensive one. This tutorial will focus on techniques for the interpretation of images of line drawings. It will cover the low level processes involved in and issues to be addressed during the segmentation and geometric description of line drawing images, consider the extraction of intermediate level entities (e.g. text, dimensions, crosshatched areas and physical outlines) and present and discuss current techniques for ground-truthing and performance evaluation. Prof. Sergey Ablameyko is Head of the Image Processing and Recognition Laboratory and Deputy Director of the Institute of Engineering Cybernetics of the National Academy of Sciences of Belarus. Dr. Tony Pridmore is Senior Lecturer in Computer Science, School of Computer Science and IT, University of Nottingham, UK, where he is a senior member of the Image Processing and Interpretation Research Group. Prof. Ablameyko and Dr. Pridmore have a combined 20 years experience of line drawing image interpretation. They have published some 100 papers in the area (independently and together) and several books, most notably S. Ablameyko & T.P. Pridmore, "Machine Interpretation of Line Drawing Images" (Springer-Verlag, 2000).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".